Project Page | Dataset (Coming soon)

Demeter is a plant parametric models that is learned from 3D scans of real-world plants. It explicitly models the plant as a graph of stem and leaf.

• Linux
• Python 3.11
• CUDA 12.1
• Pytorch 2.5.0

Install PyTorch and other dependencies.

conda create -n demeter python=3.11 -y conda activate demeter pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cu121 # basic dependencies for decoding pip install -r requirements.txt

for reconstruction from 3d point cloud, it is recommended to create a new envrionment following instruction in Pointcept

the sample data is already included in the code.

decode demeter parameter to 3d mesh of soybean

python decode.py --data_folder sample_params --sample_name 24_o --species soybean python decode.py --data_folder sample_params --sample_name 08 --species ribes python decode.py --data_folder sample_params --sample_name 10008da --species maize python decode.py --data_folder sample_params --sample_name 1 --species tobacco python decode.py --data_folder sample_params --sample_name 02 --species rose

Raw 3D point clouds -> Demeter parameters
script_reconstruction/readme.md

Raw 3D point clouds -> L-system parameteres
third_party/CropCraft/readme.md

• sample data of soybean (2025-10-7)
• decoding (2025-10-7)
• editing tutorial (TBD)
• sample data of other species (2025-11-1)
• reconstruction from 3d point cloud (2025-10-8)
• building demeter representation from your own annotated 3d point cloud (TBD)
• learning leaf shape PCA from 2D leaf scanns (TBD)
• L-system baseline (2025-10-13)
• full soybean 3d dataset (TBD)

This project is supported by NSF Awards #1847334 #2331878, #2340254, #2312102, #2414227, and #2404385. We greatly appreciate the NCSA for providing computing resources.

This code is released under the Academic Research License (Non-Commercial).
For commercial inquiries, please contact shenlong@illinois.edu.